Data is structured as a network. And now? How to analyze it? Extracting knowledge from network data is not a simple task and requires the use of appropriate tools and techniques, especially in scenarios that take into account the volume and evolving aspects of the network. There is a vast literature on how to collect, process and model social media data in the form of networks, as well as key metrics of centrality. However, there is still much to be discussed in relation to the analysis of the underlying network. In this short course we consider that data has already been collected and is already structured as a network. The goal is to discuss techniques to analyze these network data, especially considering the time perspective. First, concepts related to problem definition, temporal networks and metrics for network analysis will be presented. Next, in a more practical aspect will be shown techniques of visualization and processing of temporal networks. In the end, three case studies with real data from music playlists, Twitter and phone calls will be discussed, illustrating how to extract knowledge from temporal social networks.

Deep learning, as a subfield of machine learning, uses the strategy of creating models by stacking representation layers whose parameters are learned using known data. The central idea of this type of technique is not new, but it is recent the hype surrounding the field, caused by impressive results in particular with perception-related tasks, which were historically seen to be very difficult to be tackled by computers. Although seemingly complex methods, those are composed of simple computing elements that perform basically a chain of linear transformations, mapping subsequent vector spaces. From an algebraic formulation, this short-course presents how deep learning works from its basic components to the algorithms used to achieve learning. As case-studies the problems of classification and feature learning are presented in supervised and non-supervised scenarios using convolutional networks and auto-encoders. The objective is to provide understanding of the inner workings of such models and what makes them different from non-deep models, their theoretical advantages and limitations, as well as practical instructions for applications.

Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics.
Data science encompasses several areas such as data analytics, machine learning, statistics, optimization and managing big data.
The Meet-up will bring together women researchers and practitioners in the field to deal with the emerging challenges in processing both from theoretical and practical works on data science and advanced analytics.

For over 40 years, organization studies have examined human factors in physical workplaces and their influence on the ability of an individual to perform a task, or a set of tasks, alone or in collaboration with others. In a virtual marketplace, the crowd is typically volatile, its arrival and departure asynchronous, and its levels of attention and accuracy diverse. This has generated a wealth of new research ranging from studying workers’ fatigue in task completion to examining the role of motivation in task assignment. I will review such work and argue that we need a holistic view to take full advantage of human factors such as skills, expected wage and motivation, in improving the performance of a crowdsourcing platform.

Sentiment analysis is an ongoing field of research in text mining that deals mainly with the task of identifying the polarity (positive, negative or neutral) expressed in a piece of text. Given the recent popularity of Online Social Networks (OSNs) and other Web 2.0 applications (e.g., micro-blogs), sentiment analysis has become an important research topic, mainly when considering short and informal texts, a challenging scenario. Applications of sentiment analysis include the monitoring of reviews or opinions about a company, product or a brand; political analyses, such as the tracking of sentiments expressed by voters about candidates; among many others. In this talk, I’ll give a brief introduction to the field, present the main approaches proposed so far to deal with it, and explain their limitations and the challenges ahead, which include, for instance: ambiguity, noise, and sarcasm; lack of benchmarks; instability of the proposed methods across domains; low coverage of the methods; incompleteness of existing lexicons; etc. I’ll also present some of our solutions developed to tackle several of these issues, based mainly on novel machine learning and information retrieval techniques.